POS0258 USING REAL-TIME PATIENT-GENERATED DATA FROM MOBILE HEALTH DEVICES TO CHARACTERISE PAIN FLARES IN RHEUMATOID ARTHRITIS
Authors: T. C. Hsu, K. Druce, B. Birlie Yimer, J. Mcbeth
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Pain flares in rheumatoid arthritis (RA) often refer to episodes of increased pain severity accompanied by pain impact. Describing the prevalence and predictors of pain flare occurrence is difficult since there are no agreed classification criteria<sup>[1]</sup>. Patient-generated data collected in real-time with mobile health (mHealth) devices provide an opportunity to identify individual patterns and triggers in pain dynamics over time<sup>[2]</sup>. <h3>Objectives</h3> We aim to characterise pain flares and pre-flare exposures using real-time mHealth data from patients with RA. <h3>Methods</h3> In a 30-day mHealth study<sup>[3]</sup> we collected daily reports of pain severity on a five-point scale (ranging from none to very severe pain) via a smartphone app to define the onset and ending criteria for three types of pain flares, including 1) above average: pain severity greater than the personal median score, 2) significant change: two-point increase in pain severity from yesterday, and 3) absolute impact: two-point increase in pain severity from yesterday and pain severity greater than three. All pain flare types end when pain severity returns to the personal median score or lower. Exposures of the preceding periods were self-rated sleep quality, mood, anxiety and fatigue (all scales range 1-5, higher scores are worse) using the same study app, and passively recorded total time asleep (hr), sleep efficiency (%), sleep latency (min) and physical activity (min) via a wrist-worn accelerometer. We report the 30-day monthly pain flare rate, the average duration of pain flares and summarise average exposures one-day and three-day before pain flare onset. <h3>Results</h3> We analysed 253 participants who provided at least seven days of data (81.8% females; mean age = 59.9, average years with RA = 12.1). A total of 6,244 daily reports were included in the analysis. Pain flare occurrence decreased when applying more complex definitions. 31% of participants had pain flares under the most stringent definition of absolute impact, with two episodes per month (Table 1). Across all types, 75% of pain flares lasted two days before returning (Median = 1, IQR = 1-2) but could persist up to 11 days (Figure 1). Pre-flare exposures did not differ between pain flare types nor between the preceding periods. Participants reported fair sleep quality (Median = 3), feeling quite happy (Median = 2), not anxious (Median = 1) and mild fatigue (Median = 2) prior to pain flare onset. Objective exposures showed a daily average of 7-hour sleep with 83% efficiency, under 20 minutes to fall asleep, and approximately 50 minutes being active. <h3>Conclusion</h3> Using three increasingly complex definitions, we found pain flares were common and lasted up to 11 days based on self-reported pain severity. Future analysis should examine the role of pain impact and compare within-person exposures during pre-flare periods with non-flare periods. <h3>References</h3> [1]Beukenhorst, A. L., McBeth, J., Sergeant, J. C., Schultz, D. M., &amp; Dixon, W. G. (2018). OP0071 The prevalence of pain flares: impact of definition. [2]Gandrup, J., Selby, D. A., van der Veer, S. N., Mcbeth, J., &amp; Dixon, W. G. (2022). Using patient-reported data from a smartphone app to capture and characterize real-time patient-reported flares in rheumatoid arthritis. Rheumatology advances in practice, 6(1), rkac021. [3]McBeth, J., Dixon, W. G., Moore, S. M., Hellman, B., James, B., Kyle, S. D.,... &amp; Druce, K. L. (2022). Sleep Disturbance and Quality of Life in Rheumatoid Arthritis: Prospective mHealth Study. Journal of medical Internet research, 24(4), e32825. <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> None Declared.